Results By Agency

Code 
source(here::here("code", "setup.R"))

This section replicates the main analyses separately for subsets of comments submitted to each agency. When comparing commenters and non-commenters in this section, “non-commenters” includes organizations that did comment on Dodd-Frank rules published by other agencies.

Consumer Financial Protection Bureau (CFPB)

Wealth inequality in lobbying participation

Code 
knitr::include_graphics(here::here(c(
  "docs/CFPB/figs/nonprofit-density-1.png", 
  "docs/CFPB/figs/creditunion-density-1.png", 
  "docs/CFPB/figs/ind-assoc-density-1.png",
  "docs/CFPB/figs/FDIC-density-select-1.png",
  "docs/CFPB/figs/compustat-density-1.png")))

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(CFPB Only) Financial Resources of Organizations that Did and Did Not Comment

Organizations that spend on political campaigns

Code 
knitr::include_graphics(here::here(c(
  "docs/CFPB/figs/opensecrets-density-1.png",
  "docs/CFPB/figs/opensecrets-lobbying-density-1.png")))

Political campaign donations

Disclosed lobbying spending

(CFPB Only) Political Spending of Organizations that Did and Did Not Comment

Profit-driven organizations vs. non-profits

Code 
knitr::include_graphics(here::here("docs", "CFPB", "figs", "mp-all-predict-log-1.png"))

(CFPB Only) Predicted Probability of Participating in Dodd-Frank Rulemaking by Assets and Type of Organization
Code 
# Preferred models of commenting by wealth measures
load(here::here("models", "CFPB", "mpCompustat.Rdata"))
mpCompustat <- models[[2]]

load(here::here("models", "CFPB", "mpAll.Rdata"))
mpAll <- models[[4]]

load(here::here("models", "CFPB", "mpFDIC2.Rdata"))
mpFDIC <- models[[4]]

models <- list(mpCompustat, mpAll, mpFDIC)

rows <- tibble(
  term = c("Dependent Variable"),
  `1` = "Commented",
  `2` = "Commented",
  `3` = "Commented"
)

attr(rows, 'position') <- c(0)

modelsummary(models, notes = "Reference catagory = Banks for model 2, commercial banks for model 3")
tinytable_dico4cqih0glfuv7rjif

(CFPB Only) Log Odds of Commenting on Any Dodd-Frank Rule
(1) (2) (3)
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Reference catagory = Banks for model 2, commercial banks for model 3
Dependent Variable Commented Commented Commented
Log(Market Capitalization) 0.090***
(0.018)
Log(Assets) 0.204*** 0.207***
(0.015) (0.013)
Credit union -1.216***
(0.078)
Industry assoc. -2.482***
(0.061)
Other non-profit -4.056***
(0.039)
Log(Assets) x Credit Union 0.229***
(0.058)
Log(Assets) x Industry assoc. 1.577***
(0.259)
Log(Assets) x Other non-profit 0.839***
(0.046)
Non-commercial bank -1.019***
(0.055)
Log(Assets) x Non-commercial bank -0.060**
(0.022)
Num.Obs. 5797 495129 25670
Log.Lik. -1319.051 -22349.506 -10710.621
Code 
#TODO CREDIT UNIONS SHOULD BE IN THIS model 
knitr::include_graphics(here::here("docs", "CFPB", "figs", "mp-FDIC2-predict-1.png"))

Predicted Probability of Participating in Dodd-Frank Rulemaking by Type of Bank
Code 
knitr::include_graphics(here::here("docs", "CFPB", "figs", "mp-compustat-predict-1.png"))

(CFPB Only) Predicted Probability of Participating in Dodd-Frank Rulemaking by Market Capitalization

Frequency of participation

Code 
knitr::include_graphics(here::here(c(
  "docs/CFPB/figs/nonprofit-rules-3.png",
  "docs/CFPB/figs/creditunion-rules-3.png",
  "docs/CFPB/figs/ind-assoc-rules-3.png",
  "docs/CFPB/figs/fdic-rules-3.png",
  "docs/CFPB/figs/compustat-rules-3.png")))

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(CFPB Only) Frequent and Infrequent Commenters (By Percentile of the Number of Dockets on Which Each Organization Commented) by Resources (Log Scale)

Commenter wealth and lobbying success

Code 
knitr::include_graphics(here::here(c(
  "docs/CFPB/figs/boxplot-assets-efficacy-2.png",
  "docs/CFPB/figs/boxplot-assets-efficacy-4.png",
  "docs/CFPB/figs/boxplot-assets-efficacy-6.png",
  "docs/CFPB/figs/boxplot-assets-efficacy-10.png",
  "docs/CFPB/figs/boxplot-assets-efficacy-8.png")))
#TODO ADD CORRELATION COEFFICIENTS TO THESE PLOTS

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(CFPB Only) Amount of Text Repeated in Final Rules by Commenter Resources

Wealth and sophisticated lobbying

Code 
knitr::include_graphics(here::here(c(
  ## Nonprofits
  "docs/CFPB/figs/boxplot-assets-tech-2.png",
  ## Credit unions 
  "docs/CFPB/figs/boxplot-assets-tech-4.png",
  ## Industry Associations 
  "docs/CFPB/figs/boxplot-assets-tech-6.png",
  ## FDIC-insured banks 
  "docs/CFPB/figs/boxplot-assets-tech-10.png",
  ## Market cap (publicly-traded companies)
  "docs/CFPB/figs/boxplot-assets-tech-8.png")))

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(CFPB Only) Amount of Technical Language by Assets

Comment sophistication and lobbying success

Code 
knitr::include_graphics(here::here("docs", "CFPB", "figs", "boxplot-efficacyXsophistication-1.png")) #

(CFPB Only) Lobbying Success by Comment Sophistication
Code 
load(here::here("models", "CFPB", "mes.Rdata"))

modelsummary(models, notes = "Poisson regression yields similar results")
tinytable_f8nzrpk4th4su9pji6ab

(CFPB Only) OLS Models of Lobbying Success by Comment Sophistication
(1) (2)
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Poisson regression yields similar results
Dependent Variable Efficacy Efficacy
Technical Terms 0.118***
(0.009)
Legal Citations 19.727***
(1.759)
Log(Technical Terms) 17.733*
(7.176)
Log(Legal Citations) 211.666***
(18.733)
Num.Obs. 3890 3890
Log.Lik. -30993.016 -31263.309

Commodity Future Trading Commission (CFTC)

Wealth inequality in lobbying participation

Code 
knitr::include_graphics(here::here(c(
  "docs/CFTC/figs/nonprofit-density-1.png", 
  "docs/CFTC/figs/creditunion-density-1.png", 
  "docs/CFTC/figs/ind-assoc-density-1.png",
  "docs/CFTC/figs/FDIC-density-select-1.png",
  "docs/CFTC/figs/compustat-density-1.png")))

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(CFTC Only) Financial Resources of Organizations that Did and Did Not Comment

Organizations that spend on political campaigns

Code 
knitr::include_graphics(here::here(c(
  "docs/CFTC/figs/opensecrets-density-1.png",
  "docs/CFTC/figs/opensecrets-lobbying-density-1.png")))

Political campaign donations

Disclosed lobbying spending

(CFTC Only) Political Spending of Organizations that Did and Did Not Comment

Profit-driven organizations vs. non-profits

Code 
knitr::include_graphics(here::here("docs", "CFTC", "figs", "mp-all-predict-log-1.png"))

(CFTC Only) Predicted Probability of Participating in Dodd-Frank Rulemaking by Assets and Type of Organization
Code 
# Preferred models of commenting by wealth measures
load(here::here("models", "CFTC", "mpCompustat.Rdata"))
mpCompustat <- models[[2]]

load(here::here("models", "CFTC", "mpAll.Rdata"))
mpAll <- models[[4]]

load(here::here("models", "CFTC", "mpFDIC2.Rdata"))
mpFDIC <- models[[4]]

models <- list(mpCompustat, mpAll, mpFDIC)

rows <- tibble(
  term = c("Dependent Variable"),
  `1` = "Commented",
  `2` = "Commented",
  `3` = "Commented"
)

attr(rows, 'position') <- c(0)

modelsummary(models, notes = "Reference catagory = Banks for model 2, commercial banks for model 3")
tinytable_y7hc6e7tc5hur6wogvgh

(CFTC Only) Log Odds of Commenting on Any Dodd-Frank Rule
(1) (2) (3)
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Reference catagory = Banks for model 2, commercial banks for model 3
Dependent Variable Commented Commented Commented
Log(Market Capitalization) 0.090***
(0.018)
Log(Assets) 0.204*** 0.207***
(0.015) (0.013)
Credit union -1.216***
(0.078)
Industry assoc. -2.482***
(0.061)
Other non-profit -4.056***
(0.039)
Log(Assets) x Credit Union 0.229***
(0.058)
Log(Assets) x Industry assoc. 1.577***
(0.259)
Log(Assets) x Other non-profit 0.839***
(0.046)
Non-commercial bank -1.019***
(0.055)
Log(Assets) x Non-commercial bank -0.060**
(0.022)
Num.Obs. 5797 495129 25670
Log.Lik. -1319.051 -22349.506 -10710.621
Code 
#TODO CREDIT UNIONS SHOULD BE IN THIS model 
knitr::include_graphics(here::here("docs", "CFTC", "figs", "mp-FDIC2-predict-1.png"))

Predicted Probability of Participating in Dodd-Frank Rulemaking by Type of Bank
Code 
knitr::include_graphics(here::here("docs", "CFTC", "figs", "mp-compustat-predict-1.png"))

(CFTC Only) Predicted Probability of Participating in Dodd-Frank Rulemaking by Market Capitalization

Frequency of participation

Code 
knitr::include_graphics(here::here(c(
  "docs/CFTC/figs/nonprofit-rules-3.png",
  "docs/CFTC/figs/creditunion-rules-3.png",
  "docs/CFTC/figs/ind-assoc-rules-3.png",
  "docs/CFTC/figs/fdic-rules-3.png",
  "docs/CFTC/figs/compustat-rules-3.png")))

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(CFTC Only) Frequent and Infrequent Commenters (By Percentile of the Number of Dockets on Which Each Organization Commented) by Resources (Log Scale)

Commenter wealth and lobbying success

Code 
knitr::include_graphics(here::here(c(
  "docs/CFTC/figs/boxplot-assets-efficacy-2.png",
  "docs/CFTC/figs/boxplot-assets-efficacy-4.png",
  "docs/CFTC/figs/boxplot-assets-efficacy-6.png",
  "docs/CFTC/figs/boxplot-assets-efficacy-10.png",
  "docs/CFTC/figs/boxplot-assets-efficacy-8.png")))
#TODO ADD CORRELATION COEFFICIENTS TO THESE PLOTS

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(CFTC Only) Amount of Text Repeated in Final Rules by Commenter Resources

Wealth and sophisticated lobbying

Code 
knitr::include_graphics(here::here(c(
  ## Nonprofits
  "docs/CFTC/figs/boxplot-assets-tech-2.png",
  ## Credit unions 
  "docs/CFTC/figs/boxplot-assets-tech-4.png",
  ## Industry Associations 
  "docs/CFTC/figs/boxplot-assets-tech-6.png",
  ## FDIC-insured banks 
  "docs/CFTC/figs/boxplot-assets-tech-10.png",
  ## Market cap (publicly-traded companies)
  "docs/CFTC/figs/boxplot-assets-tech-8.png")))

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(CFTC Only) Amount of Technical Language by Assets

Comment sophistication and lobbying success

Code 
knitr::include_graphics(here::here("docs", "CFTC", "figs", "boxplot-efficacyXsophistication-1.png")) #

(CFTC Only) Lobbying Success by Comment Sophistication
Code 
load(here::here("models", "CFTC", "mes.Rdata"))

modelsummary(models, notes = "Poisson regression yields similar results")
tinytable_jqb2urcf9kl94w4035ag

(CFTC Only) OLS Models of Lobbying Success by Comment Sophistication
(1) (2)
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Poisson regression yields similar results
Dependent Variable Efficacy Efficacy
Technical Terms 0.123***
(0.004)
Legal Citations 1.628
(1.138)
Log(Technical Terms) 168.564***
(10.164)
Log(Legal Citations) 40.159***
(10.097)
Num.Obs. 1776 1776
Log.Lik. -12485.240 -12973.691

Federal Reserve (FRS)

Wealth inequality in lobbying participation

Code 
knitr::include_graphics(here::here(c(
  "docs/FRS/figs/nonprofit-density-1.png", 
  "docs/FRS/figs/creditunion-density-1.png", 
  "docs/FRS/figs/ind-assoc-density-1.png",
  "docs/FRS/figs/FDIC-density-select-1.png",
  "docs/FRS/figs/compustat-density-1.png")))

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(FRS Only) Financial Resources of Organizations that Did and Did Not Comment

Organizations that spend on political campaigns

Code 
knitr::include_graphics(here::here(c(
  "docs/FRS/figs/opensecrets-density-1.png",
  "docs/FRS/figs/opensecrets-lobbying-density-1.png")))

Political campaign donations

Disclosed lobbying spending

(FRS Only) Political Spending of Organizations that Did and Did Not Comment

Profit-driven organizations vs. non-profits

Code 
knitr::include_graphics(here::here("docs", "FRS", "figs", "mp-all-predict-log-1.png"))

(FRS Only) Predicted Probability of Participating in Dodd-Frank Rulemaking by Assets and Type of Organization
Code 
# Preferred models of commenting by wealth measures
load(here::here("models", "FRS", "mpCompustat.Rdata"))
mpCompustat <- models[[2]]

load(here::here("models", "FRS", "mpAll.Rdata"))
mpAll <- models[[4]]

load(here::here("models", "FRS", "mpFDIC2.Rdata"))
mpFDIC <- models[[4]]

models <- list(mpCompustat, mpAll, mpFDIC)

rows <- tibble(
  term = c("Dependent Variable"),
  `1` = "Commented",
  `2` = "Commented",
  `3` = "Commented"
)

attr(rows, 'position') <- c(0)

modelsummary(models, notes = "Reference catagory = Banks for model 2, commercial banks for model 3")
tinytable_2jm3e4n4ozgjy8ixul0i

(FRS Only) Log Odds of Commenting on Any Dodd-Frank Rule
(1) (2) (3)
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Reference catagory = Banks for model 2, commercial banks for model 3
Dependent Variable Commented Commented Commented
Log(Market Capitalization) 0.090***
(0.018)
Log(Assets) 0.204*** 0.207***
(0.015) (0.013)
Credit union -1.216***
(0.078)
Industry assoc. -2.482***
(0.061)
Other non-profit -4.056***
(0.039)
Log(Assets) x Credit Union 0.229***
(0.058)
Log(Assets) x Industry assoc. 1.577***
(0.259)
Log(Assets) x Other non-profit 0.839***
(0.046)
Non-commercial bank -1.019***
(0.055)
Log(Assets) x Non-commercial bank -0.060**
(0.022)
Num.Obs. 5797 495129 25670
Log.Lik. -1319.051 -22349.506 -10710.621
Code 
#TODO CREDIT UNIONS SHOULD BE IN THIS model 
knitr::include_graphics(here::here("docs", "FRS", "figs", "mp-FDIC2-predict-1.png"))

Predicted Probability of Participating in Dodd-Frank Rulemaking by Type of Bank
Code 
knitr::include_graphics(here::here("docs", "FRS", "figs", "mp-compustat-predict-1.png"))

(FRS Only) Predicted Probability of Participating in Dodd-Frank Rulemaking by Market Capitalization

Frequency of participation

Code 
knitr::include_graphics(here::here(c(
  "docs/FRS/figs/nonprofit-rules-3.png",
  "docs/FRS/figs/creditunion-rules-3.png",
  "docs/FRS/figs/ind-assoc-rules-3.png",
  "docs/FRS/figs/fdic-rules-3.png",
  "docs/FRS/figs/compustat-rules-3.png")))

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(FRS Only) Frequent and Infrequent Commenters (By Percentile of the Number of Dockets on Which Each Organization Commented) by Resources (Log Scale)

Commenter wealth and lobbying success

Code 
knitr::include_graphics(here::here(c(
  "docs/FRS/figs/boxplot-assets-efficacy-2.png",
  "docs/FRS/figs/boxplot-assets-efficacy-4.png",
  "docs/FRS/figs/boxplot-assets-efficacy-6.png",
  "docs/FRS/figs/boxplot-assets-efficacy-10.png",
  "docs/FRS/figs/boxplot-assets-efficacy-8.png")))
#TODO ADD CORRELATION COEFFICIENTS TO THESE PLOTS

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(FRS Only) Amount of Text Repeated in Final Rules by Commenter Resources

Wealth and sophisticated lobbying

Code 
knitr::include_graphics(here::here(c(
  ## Nonprofits
  "docs/FRS/figs/boxplot-assets-tech-2.png",
  ## Credit unions 
  "docs/FRS/figs/boxplot-assets-tech-4.png",
  ## Industry Associations 
  "docs/FRS/figs/boxplot-assets-tech-6.png",
  ## FDIC-insured banks 
  "docs/FRS/figs/boxplot-assets-tech-10.png",
  ## Market cap (publicly-traded companies)
  "docs/FRS/figs/boxplot-assets-tech-8.png")))

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(FRS Only) Amount of Technical Language by Assets

Comment sophistication and lobbying success

Code 
knitr::include_graphics(here::here("docs", "FRS", "figs", "boxplot-efficacyXsophistication-1.png")) #

(FRS Only) Lobbying Success by Comment Sophistication
Code 
load(here::here("models", "FRS", "mes.Rdata"))

modelsummary(models, notes = "Poisson regression yields similar results")
tinytable_1xb07kn3poun83efvzvg

(FRS Only) OLS Models of Lobbying Success by Comment Sophistication
(1) (2)
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Poisson regression yields similar results
Dependent Variable Efficacy Efficacy
Technical Terms 0.034***
(0.003)
Legal Citations 4.237***
(0.797)
Log(Technical Terms) 73.119***
(7.101)
Log(Legal Citations) 95.141***
(8.731)
Num.Obs. 1281 1281
Log.Lik. -8911.175 -8846.778

National Credit Union Administration (NCUA)

Wealth inequality in lobbying participation

Code 
knitr::include_graphics(here::here(c(
  "docs/NCUA/figs/nonprofit-density-1.png", 
  "docs/NCUA/figs/creditunion-density-1.png", 
  "docs/NCUA/figs/ind-assoc-density-1.png",
  "docs/NCUA/figs/FDIC-density-select-1.png",
  "docs/NCUA/figs/compustat-density-1.png")))

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(NCUA Only) Financial Resources of Organizations that Did and Did Not Comment

Organizations that spend on political campaigns

Code 
knitr::include_graphics(here::here(c(
  "docs/NCUA/figs/opensecrets-density-1.png",
  "docs/NCUA/figs/opensecrets-lobbying-density-1.png")))

Political campaign donations

Disclosed lobbying spending

(NCUA Only) Political Spending of Organizations that Did and Did Not Comment

Profit-driven organizations vs. non-profits

Code 
knitr::include_graphics(here::here("docs", "NCUA", "figs", "mp-all-predict-log-1.png"))

(NCUA Only) Predicted Probability of Participating in Dodd-Frank Rulemaking by Assets and Type of Organization
Code 
# Preferred models of commenting by wealth measures
load(here::here("models", "NCUA", "mpCompustat.Rdata"))
mpCompustat <- models[[2]]

load(here::here("models", "NCUA", "mpAll.Rdata"))
mpAll <- models[[4]]

load(here::here("models", "NCUA", "mpFDIC2.Rdata"))
mpFDIC <- models[[4]]

models <- list(mpCompustat, mpAll, mpFDIC)

rows <- tibble(
  term = c("Dependent Variable"),
  `1` = "Commented",
  `2` = "Commented",
  `3` = "Commented"
)

attr(rows, 'position') <- c(0)

modelsummary(models, notes = "Reference catagory = Banks for model 2, commercial banks for model 3")
tinytable_a5672natfj2l5fhotjsx

(NCUA Only) Log Odds of Commenting on Any Dodd-Frank Rule
(1) (2) (3)
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Reference catagory = Banks for model 2, commercial banks for model 3
Dependent Variable Commented Commented Commented
Log(Market Capitalization) 0.090***
(0.018)
Log(Assets) 0.204*** 0.207***
(0.015) (0.013)
Credit union -1.216***
(0.078)
Industry assoc. -2.482***
(0.061)
Other non-profit -4.056***
(0.039)
Log(Assets) x Credit Union 0.229***
(0.058)
Log(Assets) x Industry assoc. 1.577***
(0.259)
Log(Assets) x Other non-profit 0.839***
(0.046)
Non-commercial bank -1.019***
(0.055)
Log(Assets) x Non-commercial bank -0.060**
(0.022)
Num.Obs. 5797 495129 25670
Log.Lik. -1319.051 -22349.506 -10710.621
Code 
#TODO CREDIT UNIONS SHOULD BE IN THIS model 
knitr::include_graphics(here::here("docs", "NCUA", "figs", "mp-FDIC2-predict-1.png"))

Predicted Probability of Participating in Dodd-Frank Rulemaking by Type of Bank
Code 
knitr::include_graphics(here::here("docs", "NCUA", "figs", "mp-compustat-predict-1.png"))

(NCUA Only) Predicted Probability of Participating in Dodd-Frank Rulemaking by Market Capitalization

Frequency of participation

Insufficient data.

Code 
knitr::include_graphics(here::here(c(
  "docs/NCUA/figs/nonprofit-rules-3.png",
  "docs/NCUA/figs/creditunion-rules-3.png",
  "docs/NCUA/figs/ind-assoc-rules-3.png",
  "docs/NCUA/figs/fdic-rules-3.png",
  "docs/NCUA/figs/compustat-rules-3.png")))

(NCUA Only) Frequent and Infrequent Commenters (By Percentile of the Number of Dockets on Which Each Organization Commented) by Resources (Log Scale)

Commenter wealth and lobbying success

Insufficient data.

Code 
knitr::include_graphics(here::here(c(
  "docs/NCUA/figs/boxplot-assets-efficacy-2.png",
  "docs/NCUA/figs/boxplot-assets-efficacy-4.png",
  "docs/NCUA/figs/boxplot-assets-efficacy-6.png",
  "docs/NCUA/figs/boxplot-assets-efficacy-10.png",
  "docs/NCUA/figs/boxplot-assets-efficacy-8.png")))
#TODO ADD CORRELATION COEFFICIENTS TO THESE PLOTS

(NCUA Only) Amount of Text Repeated in Final Rules by Commenter Resources

Wealth and sophisticated lobbying

Insufficient data.

Code 
knitr::include_graphics(here::here(c(
  ## Nonprofits
  "docs/NCUA/figs/boxplot-assets-tech-2.png",
  ## Credit unions 
  "docs/NCUA/figs/boxplot-assets-tech-4.png",
  ## Industry Associations 
  "docs/NCUA/figs/boxplot-assets-tech-6.png",
  ## FDIC-insured banks 
  "docs/NCUA/figs/boxplot-assets-tech-10.png",
  ## Market cap (publicly-traded companies)
  "docs/NCUA/figs/boxplot-assets-tech-8.png")))

(NCUA Only) Amount of Technical Language by Assets

Comment sophistication and lobbying success

Insufficient data.

Code 
knitr::include_graphics(here::here("docs", "NCUA", "figs", "boxplot-efficacyXsophistication-1.png")) #
Code 
load(here::here("models", "NCUA", "mes.Rdata"))

modelsummary(models, notes = "Poisson regression yields similar results")

Office of the Comptroller of the Currency (OCC)

Wealth inequality in lobbying participation

Code 
knitr::include_graphics(here::here(c(
  "docs/OCC/figs/nonprofit-density-1.png", 
  "docs/OCC/figs/creditunion-density-1.png", 
  "docs/OCC/figs/ind-assoc-density-1.png",
  "docs/OCC/figs/FDIC-density-select-1.png",
  "docs/OCC/figs/compustat-density-1.png")))

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(OCC Only) Financial Resources of Organizations that Did and Did Not Comment

Organizations that spend on political campaigns

Code 
knitr::include_graphics(here::here(c(
  "docs/OCC/figs/opensecrets-density-1.png",
  "docs/OCC/figs/opensecrets-lobbying-density-1.png")))

Political campaign donations

Disclosed lobbying spending

(OCC Only) Political Spending of Organizations that Did and Did Not Comment

Profit-driven organizations vs. non-profits

Code 
knitr::include_graphics(here::here("docs", "OCC", "figs", "mp-all-predict-log-1.png"))

(OCC Only) Predicted Probability of Participating in Dodd-Frank Rulemaking by Assets and Type of Organization
Code 
# Preferred models of commenting by wealth measures
load(here::here("models", "OCC", "mpCompustat.Rdata"))
mpCompustat <- models[[2]]

load(here::here("models", "OCC", "mpAll.Rdata"))
mpAll <- models[[4]]

load(here::here("models", "OCC", "mpFDIC2.Rdata"))
mpFDIC <- models[[4]]

models <- list(mpCompustat, mpAll, mpFDIC)

rows <- tibble(
  term = c("Dependent Variable"),
  `1` = "Commented",
  `2` = "Commented",
  `3` = "Commented"
)

attr(rows, 'position') <- c(0)

modelsummary(models, notes = "Reference catagory = Banks for model 2, commercial banks for model 3")
tinytable_wyzssbi7etwhx7qu2ehi

(OCC Only) Log Odds of Commenting on Any Dodd-Frank Rule
(1) (2) (3)
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Reference catagory = Banks for model 2, commercial banks for model 3
Dependent Variable Commented Commented Commented
Log(Market Capitalization) 0.090***
(0.018)
Log(Assets) 0.204*** 0.207***
(0.015) (0.013)
Credit union -1.216***
(0.078)
Industry assoc. -2.482***
(0.061)
Other non-profit -4.056***
(0.039)
Log(Assets) x Credit Union 0.229***
(0.058)
Log(Assets) x Industry assoc. 1.577***
(0.259)
Log(Assets) x Other non-profit 0.839***
(0.046)
Non-commercial bank -1.019***
(0.055)
Log(Assets) x Non-commercial bank -0.060**
(0.022)
Num.Obs. 5797 495129 25670
Log.Lik. -1319.051 -22349.506 -10710.621
Code 
#TODO CREDIT UNIONS SHOULD BE IN THIS model 
knitr::include_graphics(here::here("docs", "OCC", "figs", "mp-FDIC2-predict-1.png"))

Predicted Probability of Participating in Dodd-Frank Rulemaking by Type of Bank
Code 
knitr::include_graphics(here::here("docs", "OCC", "figs", "mp-compustat-predict-1.png"))

(OCC Only) Predicted Probability of Participating in Dodd-Frank Rulemaking by Market Capitalization

Frequency of participation

Code 
knitr::include_graphics(here::here(c(
  "docs/OCC/figs/nonprofit-rules-3.png",
  "docs/OCC/figs/creditunion-rules-3.png",
  "docs/OCC/figs/ind-assoc-rules-3.png",
  "docs/OCC/figs/fdic-rules-3.png",
  "docs/OCC/figs/compustat-rules-3.png")))

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(OCC Only) Frequent and Infrequent Commenters (By Percentile of the Number of Dockets on Which Each Organization Commented) by Resources (Log Scale)

Commenter wealth and lobbying success

Insufficient data.

Code 
knitr::include_graphics(here::here(c(
  "docs/OCC/figs/boxplot-assets-efficacy-2.png",
  "docs/OCC/figs/boxplot-assets-efficacy-4.png",
  "docs/OCC/figs/boxplot-assets-efficacy-6.png",
  "docs/OCC/figs/boxplot-assets-efficacy-10.png",
  "docs/OCC/figs/boxplot-assets-efficacy-8.png")))
#TODO ADD CORRELATION COEFFICIENTS TO THESE PLOTS

(OCC Only) Amount of Text Repeated in Final Rules by Commenter Resources

Wealth and sophisticated lobbying

Insufficient data.

Code 
knitr::include_graphics(here::here(c(
  ## Nonprofits
  "docs/OCC/figs/boxplot-assets-tech-2.png",
  ## Credit unions 
  "docs/OCC/figs/boxplot-assets-tech-4.png",
  ## Industry Associations 
  "docs/OCC/figs/boxplot-assets-tech-6.png",
  ## FDIC-insured banks 
  "docs/OCC/figs/boxplot-assets-tech-10.png",
  ## Market cap (publicly-traded companies)
  "docs/OCC/figs/boxplot-assets-tech-8.png")))

(OCC Only) Amount of Technical Language by Assets

Comment sophistication and lobbying success

Insufficient data.

Code 
knitr::include_graphics(here::here("docs", "OCC", "figs", "boxplot-efficacyXsophistication-1.png")) #
Code 
load(here::here("models", "OCC", "mes.Rdata"))

modelsummary(models, notes = "Poisson regression yields similar results")

Securities and Exchange Commission (SEC)

Wealth inequality in lobbying participation

Code 
knitr::include_graphics(here::here(c(
  "docs/SEC/figs/nonprofit-density-1.png", 
  "docs/SEC/figs/creditunion-density-1.png", 
  "docs/SEC/figs/ind-assoc-density-1.png",
  "docs/SEC/figs/FDIC-density-select-1.png",
  "docs/SEC/figs/compustat-density-1.png")))

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(SEC Only) Financial Resources of Organizations that Did and Did Not Comment

Organizations that spend on political campaigns

Code 
knitr::include_graphics(here::here(c(
  "docs/SEC/figs/opensecrets-density-1.png",
  "docs/SEC/figs/opensecrets-lobbying-density-1.png")))

Political campaign donations

Disclosed lobbying spending

(SEC Only) Political Spending of Organizations that Did and Did Not Comment

Profit-driven organizations vs. non-profits

Code 
knitr::include_graphics(here::here("docs", "SEC", "figs", "mp-all-predict-log-1.png"))

(SEC Only) Predicted Probability of Participating in Dodd-Frank Rulemaking by Assets and Type of Organization
Code 
# Preferred models of commenting by wealth measures
load(here::here("models", "SEC", "mpCompustat.Rdata"))
mpCompustat <- models[[2]]

load(here::here("models", "SEC", "mpAll.Rdata"))
mpAll <- models[[4]]

load(here::here("models", "SEC", "mpFDIC2.Rdata"))
mpFDIC <- models[[4]]

models <- list(mpCompustat, mpAll, mpFDIC)

rows <- tibble(
  term = c("Dependent Variable"),
  `1` = "Commented",
  `2` = "Commented",
  `3` = "Commented"
)

attr(rows, 'position') <- c(0)

modelsummary(models, notes = "Reference catagory = Banks for model 2, commercial banks for model 3")
tinytable_e2gc20hm5tzdsulicwkc

(SEC Only) Log Odds of Commenting on Any Dodd-Frank Rule
(1) (2) (3)
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Reference catagory = Banks for model 2, commercial banks for model 3
Dependent Variable Commented Commented Commented
Log(Market Capitalization) 0.090***
(0.018)
Log(Assets) 0.204*** 0.207***
(0.015) (0.013)
Credit union -1.216***
(0.078)
Industry assoc. -2.482***
(0.061)
Other non-profit -4.056***
(0.039)
Log(Assets) x Credit Union 0.229***
(0.058)
Log(Assets) x Industry assoc. 1.577***
(0.259)
Log(Assets) x Other non-profit 0.839***
(0.046)
Non-commercial bank -1.019***
(0.055)
Log(Assets) x Non-commercial bank -0.060**
(0.022)
Num.Obs. 5797 495129 25670
Log.Lik. -1319.051 -22349.506 -10710.621
Code 
#TODO CREDIT UNIONS SHOULD BE IN THIS model 
knitr::include_graphics(here::here("docs", "SEC", "figs", "mp-FDIC2-predict-1.png"))

Predicted Probability of Participating in Dodd-Frank Rulemaking by Type of Bank
Code 
knitr::include_graphics(here::here("docs", "SEC", "figs", "mp-compustat-predict-1.png"))

(SEC Only) Predicted Probability of Participating in Dodd-Frank Rulemaking by Market Capitalization

Frequency of participation

Insufficient data.

Code 
knitr::include_graphics(here::here(c(
  "docs/SEC/figs/nonprofit-rules-3.png",
  "docs/SEC/figs/creditunion-rules-3.png",
  "docs/SEC/figs/ind-assoc-rules-3.png", #FIXME
  "docs/SEC/figs/fdic-rules-3.png",
  "docs/SEC/figs/compustat-rules-3.png")))

(SEC Only) Frequent and Infrequent Commenters (By Percentile of the Number of Dockets on Which Each Organization Commented) by Resources (Log Scale)

Commenter wealth and lobbying success

Code 
knitr::include_graphics(here::here(c(
  "docs/SEC/figs/boxplot-assets-efficacy-2.png",
  "docs/SEC/figs/boxplot-assets-efficacy-4.png",
  "docs/SEC/figs/boxplot-assets-efficacy-6.png",
  "docs/SEC/figs/boxplot-assets-efficacy-10.png",
  "docs/SEC/figs/boxplot-assets-efficacy-8.png")))
#TODO ADD CORRELATION COEFFICIENTS TO THESE PLOTS

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(SEC Only) Amount of Text Repeated in Final Rules by Commenter Resources

Wealth and sophisticated lobbying

Code 
knitr::include_graphics(here::here(c(
  ## Nonprofits
  "docs/SEC/figs/boxplot-assets-tech-2.png",
  ## Credit unions 
  "docs/SEC/figs/boxplot-assets-tech-4.png",
  ## Industry Associations 
  "docs/SEC/figs/boxplot-assets-tech-6.png",
  ## FDIC-insured banks 
  "docs/SEC/figs/boxplot-assets-tech-10.png",
  ## Market cap (publicly-traded companies)
  "docs/SEC/figs/boxplot-assets-tech-8.png")))

Non-profits

Credit Unions

Industry Associations

Banks

Publicly-traded Companies

(SEC Only) Amount of Technical Language by Assets

Comment sophistication and lobbying success

Code 
knitr::include_graphics(here::here("docs", "SEC", "figs", "boxplot-efficacyXsophistication-1.png")) #

(SEC Only) Lobbying Success by Comment Sophistication
Code 
load(here::here("models", "SEC", "mes.Rdata"))

modelsummary(models, notes = "Poisson regression yields similar results")
tinytable_vc7qm2zyzxolvg6uap6o

(SEC Only) OLS Models of Lobbying Success by Comment Sophistication
(1) (2)
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Poisson regression yields similar results
Dependent Variable Efficacy Efficacy
Technical Terms 0.120***
(0.006)
Legal Citations 22.052***
(2.203)
Log(Technical Terms) 87.432***
(8.155)
Log(Legal Citations) 164.646***
(14.753)
Num.Obs. 2066 2066
Log.Lik. -15303.776 -15566.844